A multi-space sampling heuristic for the vehicle routing problem with stochastic demands

The vehicle routing problem with stochastic demands consists in designing transportation routes of minimal expected cost to satisfy a set of customers with random demands of known probability distributions. This paper proposes a simple yet effective heuristic approach that uses randomized heuristics for the traveling salesman problem, a tour partitioning procedure, and a set partitioning formulation to sample the solution space and find high-quality solutions for the problem. Computational experiments on benchmark instances from the literature show that the proposed approach is competitive with the state-of-the-art algorithm for the problem in terms of both accuracy and efficiency. In experiments conducted on a set of 40 instances, the proposed approach unveiled four new best-known solutions (BKSs) and matched another 24. For the remaining 12 instances, the heuristic reported average gaps with respect to the BKS ranging from 0.69 to 0.15 % depending on its configuration

A comparative study of charging assumptions in electric vehicle routing problems

International audienceElectric vehicle routing problems (eVRPs) extend classical routing problems to consider the limited driving range of electric vehicles. In general, this limitation is overcome by introducing planned detours to battery charging stations. Most existing eVRP models rely on one (or both) of the following assumptions: (i) the vehicles fully charge their batteries every time they reach a charging station, and (ii) the battery charge level is a linear function of the charging time. In practical situations, however, the amount of charge is a decision variable, and the battery charge level is a concave function of the charging time. In this research we extend current eVRP models to consider partial charging and nonlinear charging functions. We present a computational study comparing our assumptions with those commonly made in the literature. Our results suggest that neglecting partial and nonlinear charging may lead to infeasible or overly expensive solutions

A parallel matheuristic for the technician routing problem with electric and conventional vehicles

The technician routing problem with conventional and electric vehicles (TRP-CEV) consists in designing service routes taking into account the customers’ time windows and the technicians’ skills, shifts, and lunch breaks. In the TRP-CEV routes are covered using a fixed and heterogeneous fleet of conventional and electric vehicles (EVs). Due to their relatively limited driving ranges, EVs may need to include in their routes one or more recharging stops. In this talk we present a parallel matheuristic for the TRP-CEV. The approach works in two phases. In the first phase it decomposes the problem into a number of “easier to solve” vehicle routing problems with time windows and solves these problems in parallel using a GRASP. During the execution of this phase, the routes making up the local optima are stored in a long-term memory. In the second phase, the approach uses the routes stored in the long-term memory to assemble a solution to the TRP-CEV. We discuss computational experiments carried on real-world TRP-CEV instances provided by a French public utility and instances for the closely-related electric fleet size and mix vehicle routing problem with time windows and recharging stations taken from the literature.

The electric vehicle routing problem with partial charging and nonlinear charging function

Electric vehicle routing problems (eVRPs) extend classical routing problems to consider the limited driving range of electric vehicles. In general, this limitation is overcome by introducing planned detours to battery charging stations. Most existing eVRP models rely on one (or both) of the following assumptions: (i) the vehicles fully charge their batteries every time they reach a charging station, and (ii) the battery charge level is a linear function of the charging time. In practical situations, however, the amount of charge is a decision variable, and the battery charge level is a concave function of the charging time.In this paper we extend current eVRP models to consider partial charging and nonlinear charging functions. We present a computational study comparing our assumptions with those commonly made in the literature. Our results suggest that neglecting partial and nonlinear charging may lead to infeasible or overly expensive solutions

A simple hybrid heuristic for the Green Vehicle Routing Problem

International audienc